innovation
Interesting Article about ADHD and Creativity
This “pocket” story is from Scientific American, originally published on March 5, 2019. The Creativity of ADHD.
Over the years, I have found that some of the most interesting, creative, and effective CEOs have ADHD-like tendencies. Strangely, they may not even be aware that they have it. Hyperfocus can be incredibly effective when attached to a driven person.
Below are links to a couple of posts that make these concepts and their benefits tangible:
These are examples of why it is best to look beyond labels, set aside preconceived notions, and explore each individual’s potential to contribute. The best managers and leaders tend to have this ability.
If you want to take it a step further, let it guide you in finding the best approach to teaching, coaching, and motivating your team members. It may take a little extra effort, but the results are amazing.
Biometric Identity Theft
Recently, I have been researching the potential for fraud and identity theft using fingerprints from photos posted on social media. Last week, Amazon released its “Amazon One” Palm Scanner to pay for purchases. That announcement made me wonder: What are the potential implications for fraud and identity theft using biometric data taken from images?
Technology continues to improve rapidly, which often means, “Where there is the will, there’s a way.”
There are a surprising number of ways to accurately identify someone from a photo or video. Moreover, technology can copy fingerprints from social media photos taken up to three meters away. New technology has been proven effective at using 3D printing technology to create “fake fingerprints” that bypass many fingerprint scanners. Other factors may be at play (such as temperature), but that is speculation at this point.
Since fingerprints can be copied from photos taken up to three meters away, can a palm print be copied from a photo taken 5-10 meters away? That question led to an interesting but unscientific experiment where I took pictures of my hand, enlarged them, measured the distance between the ridges and furrows of my fingers and my palm, and compared the results. Spoiler – probably not (yet) – but likely not far away given the rapid advancement of AI.
Several areas showed similar distances for both my fingers and palm. But there were also areas on my palm where the average distance between “landmarks” was 3-5+ times greater. It turns out that a palm image is often segmented into 3-4 distinct regions for identification purposes, likely due to this type of variation. This link helped me understand the process.
This research led to an idea for a chip-based embedded filter for smart devices and laptops. It would obfuscate key biometric information when extracting the data for display without affecting the integrity of the original stored image. This functionality would automatically provide an additional layer of privacy and data protection. It would require optimized object detection capabilities (possibly R-CNN) that were highly efficient and run on a capable but low-energy processor like the ARM Cortex-M. Retraining and upgrades would be accomplished with firmware updates.
Edit 2020-10-13: This article on “Tiny ML” from Medium.com is the perfect tie-in to the abovementioned idea.
While Amazon’s technology is much newer and presumably at least partially based on their 2019 Patent Application (which does look impressive), it makes you wonder how susceptible these devices might be to fraud given reports of the scans occurring “almost instantaneously.” Speed is one aspect of successful large-scale commercial adoption, but from my perspective, the system’s accuracy and integrity matter far more.
Time will tell how robust and foolproof Amazon’s new technology really is. Given their reach, this could occur sooner rather than later. Ultimately, multiple forms of biometric scans (such as a full handprint with shape, palm, and fingerprints or a retina scan 2-3 minutes prior to the palm scan to maintain performance) may be required for enhanced security, especially with mobile devices.
Additional Resources:
- An in-depth NIH document that helps explain the process and issues.
- A Simplified Guide to Fingerprint Analysis
- This is a 2012 Fingerprint and Palmprint Recognition Approach Report from the European Journal of Scientific Research that has many images and very good explanations.
- “A Textural Approach to Palmprint Identification“
Could a New Channel Model Lead to Sales Amplification?
Over the years, I have helped successful companies and start-ups improve and strengthen their Channel and Strategic Alliances programs. The results have been good, but could they have been better? Keep reading to learn about the benefits of enhanced business ecosystems.
Most traditional channel models support Distributors, Resellers, OEMs, and ISVs. The business mainly flows upwards to the main vendor. If that vendor has popular, widely used products (think Microsoft and Oracle), partner business can be good because the demand stays consistent. But sales pipelines suffer when that is not the case.
Sales Channel business is not the main source of revenue for most companies, but it can become the largest and most scalable revenue source for nearly any business. Just think about the money left on the table by not adopting a growth mindset and executing a new and better strategy.
In the summer of 2016, I attended the “Sage Summit” in Chicago. It was impressive to see the Sage Group’s efforts to build, strengthen, and protect their Customers and Channel Partners community. They tried to foster higher levels of collaboration between the various types of partners – implementation services, consulting, staff augmentation services, complementary product vendors, etc. They had created their own highly successful Business Ecosystem, which is an excellent proof point.
When designing a channel partner program, my focus has always been on balancing promoting and protecting partners’ business with ensuring end customers have the best possible experience (and some recourse when things do not work out as expected). I have used a variety of methods to accomplish those goals, including creating a systematic approach to seeding relationships between partners’ complementary offerings and facilitating even greater business activity.
Nearly a year ago, I began working with a management consultancy run by Robert Kim Wilson, whose business vision is based on his book, “They Will Be Giants.” Links for this book and other relevant resources are provided at the bottom of the post. Kim asserts that Entrepreneurs with a Purpose-Driven Business Ecosystem (PDBE) are more successful than those without one, and he provides examples to support his point. Having experienced Kim’s PDBE, I see how purpose fosters trust and collaboration.
As I did more research, I found that thought leaders in this space have increasingly focused on Business Ecosystems and Business Ecosystem Organizers (such as Sage in the earlier example). Those findings reinforced the PDBE approach, and external validation like this is always good.
From my perspective, it was just as important that this concept apply to businesses of any size – especially for small to midsize businesses. The fun part for me is exploring a specific business, analyzing what they do today, and quantifying the potential benefits of adopting this new strategy.
So, how does this new type of Business Ecosystem work?
- The Business Ecosystem Organizer expands the overall network, vets new “Business Ecopartners,” and provides a framework or infrastructure for the various Business Ecopartners to get to know one another, exchange ideas, and discuss opportunities.
- This can become an incredibly sustainable revenue source for companies willing to invest time to collaborate and share ideas in order to grow and support the Business Ecosystem.
- Business Ecopartners will have access to trusted resources to augment existing business and take on new, bigger projects by leveraging the available expertise.
- Suppose that you have products or services that work with commercial CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), or SCM (Supply Chain Management), and have seen a growing demand for functionality that relies on highly specialized technologies like:
- Cryptocurrency support.
- Blockchain for financial transactions and things like traceability in your supply chain or IoT data.
- AI (artificial intelligence) and ML (machine learning) to detect patterns and anomalies – such as fraud detection, Deep Learning/Neural Networks for image recognition or other complex pattern recognition.
- Graph databases to better understand a business and infer new ways to improve it.
- Knowledge Graph/Semantic databases to create deeper meaning and understanding with data from multiple sources – assisting in deeper understanding and Transfer Learning (which also has an AI tie-in).
- Building these practices in-house would not be practical or cost-effective for most businesses, so partnering becomes very attractive.
- This type of business relationship can also be very attractive to a Business Ecopartner because someone else handles prospecting, sales, billing, account management, etc.
- Suppose that you have products or services that work with commercial CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), or SCM (Supply Chain Management), and have seen a growing demand for functionality that relies on highly specialized technologies like:
- Other Business Ecopartners can leverage your products or services for their projects and engagements, expanding their addressable market and creating additional revenue sources for the other ecopartners.
- By actively participating in this network, any business can now compete on imagination and innovation – providing a more comprehensive solution that could become a major source of differentiation from their competitors.
Value realized from this New Business Ecosystem model:
- These new sources of business and talent can become a real competitive advantage for your business.
- This becomes the source for Sales Amplification because each business is, directly and indirectly, expanding its reach and growth potential.
- The weighted (based on capabilities, capacity, responsiveness, and Ecopartner feedback) Business Ecopartner network model could lead to exponential business growth – a winning strategy for any business.
Next Steps
If this sounds interesting and you would like to discuss how it could look for your business, contact me to schedule an exploration call.
References:
- https://kimwilson5.wixsite.com/theywillbegiants/the-book
- https://www.bcg.com/publications/2019/emerging-art-ecosystem-management.aspx
- https://www.gartner.com/smarterwithgartner/8-dimensions-of-business-ecosystems/
- https://sloanreview.mit.edu/article/the-myths-and-realities-of-business-ecosystems/
- https://www2.deloitte.com/us/en/pages/operations/articles/business-ecosystems.html
- https://www.accenture.com/_acnmedia/pdf-56/accenture-strategy-your-role-in-the-ecosystem.pdf
- https://www.bain.com/insights/shifting-from-assets-to-ecosystems-video/
- https://hbr.org/2019/09/in-the-ecosystem-economy-whats-your-strategy
IoT and Vendor Lock-in
I was researching an idea last weekend and stumbled across something unexpected. My view on IoT has been that it provides a framework for a rich ecosystem of hardware and software products and their use. That flexibility and extensibility foster innovation, which in turn leads to greater use and adoption of the best products. It was quite a surprise to discover that IoT was being used to do just the opposite.
My initial find was a YouTube video about “Tractor Hacking” that lets farmers make their own repairs. That seemed like an odd video to appear in my search results, but it made sense about halfway through. The video discusses not having access to software, replacement components not working because they aren’t registered to that tractor’s serial number, and the only alternative being costly transportation of the equipment to a Dealership to have a costly component installed.
I initially thought there had to be more to the story, as I found it hard to believe that a major vendor in any industry would intentionally do something like this. That led me to an article from nearly two years earlier that contained the following:
“IoT to completely transform their business model” and
“John Deere was looking for ways to change their business model and extend their products and service offering, allowing for a more constant flow of revenue from a single customer. The IoT allows them to do just that.”
That article closed with the assertion:
“Moreover, only allowing John Deere products access to the ecosystem creates a buyer lock-in for the farmers. Once they own John Deere equipment and make use of their services, it will be very expensive to switch to another supplier, thus strengthening John Deere’s strategic position.”
While any technology – especially platforms – has the potential for vendor lock-in, the majority of vendors offer some form of openness, such as:
- Supporting open standards, APIs, and processes that support some degree of portability and third-party product access.
- Providing simple ways to unload your data in at least one of several commonly used non-proprietary formats.
Some buyers may deliberately implement systems that support non-standard technology and extensions because they believe the long-term benefits of a tightly coupled system outweigh the risks of being locked into a vendor’s proprietary stack. But there are almost always several competitive options available; always consider all viable alternatives.
Less technology-savvy buyers may never even consider asking questions like this when purchasing. Even technologically savvy people may fail to consider IoT as a key component of everyday tools and services – thus failing to recognize the implications of a closed system relative to their purchase.
It will be interesting to see whether deliberate business strategies like these change because of competitive pressure, social pressure, or legislation over the coming years. In the meantime, the principle of caveat emptor may be truer than ever in this age of connected everything and the Internet of Things.
Good Article on Why AI Projects Fail
Today I came across this very good article focused on lessons learned, which could help anyone interested in these topics. It included a good mix of non-technical problems.
This is the link to the article, along with my commentary on the Top 3 items listed: https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html
Item #1:
The article discusses how the “problem” being evaluated was misstated using technical terms. At least some of these efforts are conducted “in a vacuum.” Given the cost and strategic importance of getting these early-adopter AI projects right, that surprised me.
In Sales and Marketing, you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither vet the solution initially nor quickly qualify the need for it when speaking with customers or prospects. That leaves room for error when transitioning from strategy to execution.
More collaboration with Business likely would have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. This should have been called out early as a major lesson learned.
Item #2:
This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.
That was interesting because assumptions may have played a role in overlooking other aspects of the problem, or the teams may have been overly confident they could get the right results with the data available. In the examples cited, those teams identified the problems and took corrective action. A follow-up article describing the process used to determine the root cause in each case would be very interesting.
As an aside, from my perspective, this is why Explainable AI is so important. Sometimes, you just don’t know what you don’t know (the unknown unknowns). Understanding why and on what the AI is basing its decisions should help provide better-quality curated data up front, as well as identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.
Item #3:
This didn’t surprise me, but it should be a cause for concern as advances are made at faster rates and organizations race to be first to market with an AI-based competitive advantage, potentially with less validation than ideal. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.“
Bonus Item:
The discussion about the value of unstructured data was very interesting, especially when you consider:
- The potential for NLU (natural language understanding) products in conjunction with ML and AI.
- This is a great NLU-pipeline diagram from North Side Inc. in Canada, one of the pioneers in this space.
- The importance of semantic data analysis relative to any ML effort.
- The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
- I personally believe the biggest exception to this assertion will be GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond traditional CPU-based products, and have geospatial capabilities that add an extra dimension of insight to the problem being solved.
Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy
This is an exciting space that will grow significantly over the next 3-5 years. The more information, experiences, and lessons learned are shared, the better it will be for everyone.
- ← Previous
- 1
- 2
- 3
- …
- 5
- Next →
